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Copyright of Mercados y Negocios is the property of Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Dearq is the property of Universidad de los Andes and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is no learning without mistakes. However, there is a clash between 'positive attitudes and beliefs' regarding learning processes and the 'negative attitudes and beliefs' toward these being accompanied by mistakes. This clash exposes a cognitive bias toward mistakes that might block personal and organizational learning. This study presents an advanced measurement method to assess the bias of mistakes. The essence of it is the detection of the existing contradictions between attitude and behavior toward mistakes at the personal and organizational levels, as well as combined. This study is based on empirical evidence from a sample of 768 knowledge workers, divided into biased and non-biased subsamples following the procedure proposed in this paper. Those subsamples were next applied to the structural model, examining knowledge, learning, and collaboration cultures (the KLC approach) 's influence on organizational intelligence to validate the proposed method. Results showed that the applied method efficiently detects the DBM and exposes that in doubly mistakes-biased knowledge-driven organizations, the influence of knowledge culture on the mistakes acceptance component of learning culture is negative. So, organizations with a dominated double bias of mistakes do not accept the affirmation of learning from mistakes. Summing up, this study constitutes the Double Bias of Mistakes Theory, which states that the clash between positive attitudes and beliefs regarding learning processes and negative attitudes and beliefs toward mistakes exposed by focusing on control managers (bosses) might block organizational learning from mistakes and, as a consequence, negatively affect organizational intelligence. Without the empirical support for this theory, there was a risk that the idea of accepting mistakes as a potential source of learning would be simplified by biased minds to mistakes tolerance and rejected as ridiculous. Accepting that mistakes can be a source of precious learning does not equal mistake tolerance. On the contrary, it is the first step to managing mistakes and creating efficient error avoidance systems thanks to lessons learned from failures. This study introduces the method of measurement and detection of the Double Bias of Mistakes phenomenon, contributing to the science of organizational learning and collective intelligence-building. [ABSTRACT FROM AUTHOR]

IntroductionThe construct of collective intelligence assumes that groups have a better capacity than individuals to deal with complex, poorly defined problems. The digital domain allows us to analyze this premise under circumstances different from those in the physical environment: we can gather an elevated number of participants and generate a large quantity of data.MethodsThis study adopted an emotional perspective to analyze the interactions among 794 adolescents dealing with a sexting case on an online interaction platform designed to generate group answers resulting from a certain degree of achieved consensus.ResultsOur results show that emotional responses evolve over time in several phases of interaction. From the onset, the emotional dimension predicts how individual responses will evolve, particularly in the final consensus phase.DiscussionResponses gradually become more emotionally complex; participants tend to identify themselves with the victim in the test case while increasingly rejecting the aggressors.

Addressing the global water quality challenges requires collaborative efforts, multidisciplinary approaches, and innovative solutions. Here we report on the success of a special collective intelligence “hackathon event,” organized by five United Nations agencies and the European Commission, with the aim of reinventing engagement with diverse experts and stakeholders to tackle real-world challenges in water quality monitoring and assessment. Participants from diverse backgrounds and regions convened to devise inventive solutions in four key challenge areas, including (1) transformation of water quality data into water stewardship action, (2) empowering citizen scientists to improve water quality, (3) incorporation of Indigenous communities and their water quality knowledge in global information systems, and (4) routine monitoring of antimicrobial resistance in water. The hackathon approach fosters collective intelligence in a safe, creative and collaborative environment, enabling participants to harness their collective knowledge, expertise and skills. Key outcomes were conceptualizing practical frameworks and tailored toolboxes for diverse water quality innovations to improve monitoring, empower communities, and support policy-making. Emphasis was placed on the purpose and value of interdisciplinary collaborations to address complex global challenges, showcasing synergies between technology, environmental science, and social engagement. Hackathons are catalysts for collaborative innovation which unlock future endeavors in harnessing collective intelligence to safeguard our most precious resource – water.

BackgroundStudies report higher diagnostic accuracy using the collective intelligence (CI) of multiple clinicians compared with individual clinicians. However, the diagnostic process is iterative, and unexplored is the value of CI in improving clinical recommendations leading to a final diagnosis.MethodsTo compare the appropriateness of diagnostic recommendations advised by individual physicians versus the CI of physicians, we entered actual consultation requests sent by primary care physicians to specialists onto a web-based CI platform capable of collecting diagnostic recommendations (next steps for care) from multiple physicians. We solicited responses to 35 cases (12 endocrinology, 13 gynecology, 10 neurology) from ≥3 physicians of any specialty through the CI platform, which aggregated responses into a CI output. The primary outcome was the appropriateness of individual physician recommendations versus the CI output recommendations, using recommendations agreed upon by 2 specialists in the same specialty as a gold standard. The secondary outcome was the recommendations' potential for harm.ResultsA total of 177 physicians responded. Cases had a median of 7 respondents (interquartile range: 5-10). Diagnostic recommendations in the CI output achieved higher levels of appropriateness (69%) than recommendations from individual physicians (45%; χ2 = 5.95, P = 0.015). Of the CI recommendations, 54% were potentially harmful, as compared with 41% of individuals' recommendations (χ2 = 2.49, P = 0.11).LimitationsCases were from a single institution. CI was solicited using a single algorithm/platform.ConclusionsWhen seeking specialist guidance, diagnostic recommendations from the CI of multiple physicians are more appropriate than recommendations from most individual physicians, measured against specialist recommendations. Although CI provides useful recommendations, some have potential for harm. Future research should explore how to use CI to improve diagnosis while limiting harm from inappropriate tests/therapies.

Abstract Background This paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to support understanding and primary use of complex national data but also to increase the dissemination and secondary impact of research based on these data. The case study uses the Northern Ireland Longitudinal Study (NILS), a member of the UK family of census/administrative data longitudinal studies (UKLS). Methods A stakeholder-engaged CI approach was applied to inform the transformation of the NILS Research Support Unit (RSU) infrastructure to support researchers in their use of government data, including collaborative decision-making and better dissemination of research outputs. Results We provide an overview of NILS RSU infrastructure design changes that have been implemented to date, focusing on a website redesign to meet user information requirements and the formation of better working partnerships between data users and providers within the Northern Ireland data landscape. We also discuss the key challenges faced by the design team during this project of transformation. Conclusion Our primary objective to improve government data infrastructure and to increase dissemination and the impact of research based on data was a complex and multifaceted challenge due to the number of stakeholders involved and their often conflicting perspectives. Results from this CI approach have been pivotal in highlighting how NILS RSU can work collaboratively with users to maximize the potential of this data, in terms of forming multidisciplinary networks to ensure the research is utilized in policy and in the literature and providing academic support and resources to attract new researchers.

Since its establishment in 2014, Data for Policy (https://dataforpolicy.org) has emerged as a prominent global community promoting interdisciplinary research and cross-sector collaborations in the realm of data-driven innovation for governance and policymaking. This report presents an overview of the community’s evolution from 2014 to 2023 and introduces its six-area framework, which provides a comprehensive mapping of the data for policy research landscape. The framework is based on extensive consultations with key stakeholders involved in the international committees of the annual Data for Policy conference series and the open-access journal Data & Policy (https://www.cambridge.org/core/journals/data-and-policy), published by Cambridge University Press. By presenting this inclusive framework, along with the guiding principles and future outlook for the community, this report serves as a vital foundation for continued research and innovation in the field of data for policy.

Macroprogramming refers to the theory and practice of expressing the macro(scopic) behaviour of a collective system using a single program. Macroprogramming approaches are motivated by the need of effectively capturing global/system-level aspects and the collective behaviour of multiple computational components, while abstracting over low-level details. Previously, this programming style had been primarily adopted to describe the data-processing logic in sensor networks; recently, research forums on spatial computing, collective systems, and the Internet of Things have provided renewed interest in macro approaches. However, related contributions are still fragmented and lack conceptual consistency. Therefore, to foster principled research, an integrated view of the field is provided, together with opportunities and challenges. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Applied Business & Economics (JABE) is the property of Universitas Indraprasta PGRI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Recherches en Sciences de Gestion is the property of ISEOR and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. This is exacerbated in public administrations, where knowledge should be oriented towards public services. In this study, an artificial intelligence-based method for the evaluation of knowledge is described. This method takes advantage of the structure and contents of the knowledge representation schemas (representing the knowledge of the corresponding experts) to carry out knowledge evaluation. More precisely, the method allows the various comparisons between the schemas to be integrated and the overall schema to evaluate the contribution of each schema. [ABSTRACT FROM AUTHOR]

In this research methods essay, we describe Interactive Management Research (IMR), a participatory action research methodology with extensive applications in organizational settings but new to organizational communication research. IMR offers possibilities as a participant-centered methodology that is particularly well suited for complex organizational design situations requiring a systems perspective. We detail two versions of IMR, an interview-based method (IMRi) and a group-based method (IMRg), using a case study of each method to illustrate their application to organizational communication research. We believe IMR is an approach to participatory action research that can provide unique insights into the systems thinking and communication that shapes organizations and organizing. [ABSTRACT FROM AUTHOR]

We study the connection between communication network structure and an organization's collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed—interferes with collective problem-solving by obstructing the integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms responsible for that poor integration of ideas, information, and solutions would nevertheless prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an inferior consensus, requiring a collective shift of solution after more information emerged. We find that when the communication network within an organization is more centralized, it achieves the benefits of connectivity (spread of novel better solutions) without the costs (getting stuck on an existing inferior solution). We also find, however, that these benefits of centralization only materialize in networks with two-way flow of information and not when information only flows from the center of the network outward (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to reconceptualize theory on the impact of centralization—and how it affects conformity pressure (lock-in) and awareness of diverse ideas (learning)—on collective problem-solving that demands adaptation. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: The authors acknowledge financial support from the Division of Research at the Harvard Business School for making this research possible. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals’ and collectives’ freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi‐agent reinforcement learning to simulate dynamics within a human–machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q‐learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies. [ABSTRACT FROM AUTHOR]

Developing system understanding and testing interventions are critical steps to addressing wicked problems. Fuzzy cognitive mapping (FCM) can be a useful participatory modeling tool that enables aggregation of individual perspectives to build system models that represent groups' collective intelligence (CI). However, current FCM aggregation methodologies for creating CI models have rarely been tested and compared. We conducted 51 FCM interviews with local experts in the Flint, MI food system to map their mental models about how different food system sectors influenced desirable outcomes. Using four differing aggregation techniques, based on experts' identity diversity and cognitive diversity, we generated four CI models. The models were compared based on their similarity to real-world complex systems using performance metrics like network structure, micro-motifs, cognitive distance, and scenario outcomes. We found that using cognitive diversity to group individuals was better suited for modeling systems with diverse holders of knowledge. [ABSTRACT FROM AUTHOR]

Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here, we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or noncompetitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions. [ABSTRACT FROM AUTHOR]

With the recent development of agriculture, the growing area and utilization rate of facilities are increasing, but it is necessary to control and prevent pests, and if the disease is detected at an early stage, appropriate treatment is possible. To this end, researches on control systems using artificial intelligence are being expanded recently, therefore we propose a pest diagnosis system using data acquisition and deep learning through collective intelligence. This study modeled the diagnostic system based on deep learning using the collective intelligence that the user group participates in the prediction of pests arising from the plant cultivation and the data registered by experts in the field. Diagnostic data were collected information on pest diagnosis registered on the Internet and used; the collected data were constructed as a data set that is easy to analyze, through preprocessing, types of crops were classified, pests data were studied through TensorFlow. Most of the researches for the control and prevention of pests are based on web-based expert system. In this paper, we collect data through the collective intelligence and the general public. Especially, when a user uses input question and answers data without a formalized format, it gives wrong prediction; therefore, the preprocessing process was performed for data analysis because it could adversely affect the reliability of the system. After the data collection and preprocessing process is completed, a prediction model is created using TensorFlow, an artificial intelligence open source framework, using the generated data set. The user was allowed to input arbitrary data values while testing the data one to five times based on the data value and the effective value of the prediction model was confirmed according to the change of the value. Through the research, it is proved that diagnosis of pests is possible by using collective intelligence. In future research, research on the construction of a system that enables users to diagnose pests more easily should be continued using not only collective intelligence but also image and video. [ABSTRACT FROM AUTHOR]

Collectiveness is an important property of many systems—both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals or even to produce intelligent collective behavior out of not-so-intelligent individuals. Indeed, collective intelligence, namely, the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems—motivated by recent technoscientific trends like the Internet of Things, swarm robotics, and crowd computing, to name only a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognized research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this article considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering. [ABSTRACT FROM AUTHOR]

Abstract Background In many dental settings, diagnosis and treatment planning is the responsibility of a single clinician, and this process is inevitably influenced by the clinician’s own heuristics and biases. Our aim was to test whether collective intelligence increases the accuracy of individual diagnoses and treatment plans, and whether such systems have potential to improve patient outcomes in a dental setting. Methods This pilot project was carried out to assess the feasibility of the protocol and appropriateness of the study design. We used a questionnaire survey and pre-post study design in which dental practitioners were involved in the diagnosis and treatment planning of two simulated cases. Participants were provided the opportunity to amend their original diagnosis/treatment decisions after viewing a consensus report made to simulate a collaborative setting. Results Around half (55%, n = 17) of the respondents worked in group private practices, however most practitioners (74%, n = 23) did not collaborate when planning treatment. Overall, the average practitioners’ self-confidence score in managing different dental disciplines was 7.22 (s.d. 2.20) on a 1–10 scale. Practitioners tended to change their mind after viewing the consensus response, particularly for the complex case compared to the simple case (61.5% vs 38.5%, respectively). Practitioners' confidence ratings were also significantly higher (p < 0.05) after viewing the consensus for complex case. Conclusion Our pilot study shows that collective intelligence in the form of peers’ opinion can lead to modifications in diagnosis and treatment planning by dentists. Our results lay the foundations for larger scale investigations on whether peer collaboration can improve diagnostic accuracy, treatment planning and, ultimately, oral health outcomes.

The presence of Collective Intelligence engenders the integrity of the person and the group in the business and thus, synergistically strengthens the collective resilience, understood as the individual and the company’s resilience. Synergistically as shown, is the habit of creative interactively cooperation, so that each element or party increases the effect of the other.

Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on fault-tolerant quantum computing paradigms cannot be experimentally verified in the short term. The variational quantum algorithm design paradigm can better adapt to the practical characteristics of noisy quantum hardware and is currently one of the most promising solutions. However, variational quantum algorithms, due to their highly entangled nature, encounter the phenomenon known as the “barren plateau” during the optimization and training processes, making effective optimization challenging. This paper addresses this challenging issue by researching a variational quantum neural network optimization method based on collective intelligence algorithms. The aim is to overcome optimization difficulties encountered by traditional methods such as gradient descent. We study two typical applications of using quantum neural networks: random 2D Hamiltonian ground state solving and quantum phase recognition. We find that the collective intelligence algorithm shows a better optimization compared to gradient descent. The solution accuracy of ground energy and phase classification is enhanced, and the optimization iterations are also reduced. We highlight that the collective intelligence algorithm has great potential in tackling the optimization of variational quantum algorithms.

This paper presents IEML (Information Economy MetaLanguage), a uniform representation of human meaning and knowledge that can be read and processed automatically by machines. Distinguished from pragmatic and referential semantics, linguistic semantics is today incompletely formalised. Only its syntagmatic dimension has been mathematised in the form of regular languages. Its paradigmatic dimension remained to be formalised. To solve the problem of the complete mathematising of language, including its paradigmatic dimension, I propose to code the linguistic meaning in IEML. IEML has the same expressive capacity as a natural language and has an algebraic structure allowing the calculation of its semantics. The article explains its dictionary, its formal grammar, and its integrated tools for building semantic graphs. As far as its applications are concerned, IEML could be the vector of a fluid calculation and communication of meaning – semantic interoperability – capable of decompartmentalising the digital memory and feeding the progress of collective intelligence, artificial intelligence, and digital humanities. I conclude by indicating some research directions. This paper presents a synthesis of several decades of research.